Using personalized physiological parameters for sleep/wake detection

ABSTRACT

Aspects of the present disclosure provide methods, apparatuses, and systems for accurately determining sleep and wake onset based on a user&#39;s personalized physiological parameters for sleep and wake. First, a user is determined to be asleep using population level data. Thereafter, sensor collected data is used to determine the user&#39;s distribution of values of a physiological parameter when the user is asleep. This distribution of values is then used, instead of population-level data, to determine the user is asleep in real-time. As a result, the content and interventions are provided to help users get back to sleep. Further, the described techniques allow more accuracy in determining sleep statistics which can guide recommended interventions and therapies.

FIELD

Aspects of the present disclosure generally relate to methods,apparatuses, and systems for determining a user is awake or asleep basedon personalized, user-specific values of a physiological feature.

BACKGROUND

Wearable devices are used to track activity, monitor health, and helppeople sleep better. Current sleep detection and sleep tracking methodsmay estimate a duration of sleep; however, they lack the ability todetermine real-time sleep onset or precisely when a user awakes. Thereis a need to accurately determine when a user falls asleep or wakes upin order to provide a personalized user experience responsive to whenthe user falls asleep or wakes up.

SUMMARY

Instead of relying primarily on population-level statistics, themethods, apparatuses, and systems described herein use user-specificvalues of a measured physiological feature to accurately determine whena user has fallen asleep or woken up.

During an initialization stage, population-level statistics are used todetermine that a user is, for example, asleep. When the user isdetermined to be asleep, user-specific physiological parameters aremeasured and associated with the user being asleep. When the user isdetermined to be awake, either using population-level statistics orbased on user-input indicating the user is awake, user-specificphysiological parameters are measured and correlated with the user beingawake.

After the initialization stage, during the steady-state stage, the userspecific values of the physiological parameters are used to determine,virtually in real-time, whether the user is awake or asleep. In responseto determining the user is awake or asleep, one or more actions aretaken to provide the user with a customized experience. In an aspect,when the user is determined to be asleep, audio, visual output, and/orhaptic output is adjusted by fading out a relaxation or guided breathingexercise or outputting masking sounds. In an aspect, when the user isdetermined to be awake, the user is prompted to stand up (and get out ofbed) to promote better sleep habits or a guided breathing or relaxationexercise is output to help the user fall back asleep.

In an aspect, a method for creating a personalized audio experience isprovided. The method includes determining a distribution ofuser-specific asleep values for a physiological feature when a user isdetermined to be asleep, determining a distribution of user-specificawake values for the physiological feature when the user is determinedto be awake, determining the user is asleep when a measured value of thephysiological feature extracted from a real-time physiological signal isin the distribution of user-specific asleep values, determining the useris awake when a measured value of the physiological feature extractedfrom the real-time physiological signal is in the distribution ofuser-specific awake values, and altering an audio experience based ondetermining the user is asleep or awake.

According to aspects, altering the audio experience in response todetermining the user is awake comprises initiating an experience to helpguide the user to sleep.

According to aspects, the method further comprises determining the userhas been awake for a threshold amount of time, wherein altering theaudio experience comprises instructing the user to stand up when theuser is determined to be awake for the threshold amount of time.

According to aspects, the method further comprises entering a low-powermode by one or more sensors in response to determining the user isasleep.

In an aspect, a method for creating a personalized audio experience isprovided. The method comprises, during an initialization stage:measuring a value of a physiological feature associated with a userbased on a sensor signal, determining the user is asleep usingpopulation-level data and the measured value of the physiologicalfeature, when the user is determined to be asleep, measuring values ofthe physiological feature extracted from a physiological signal obtainedusing the sensor signal, determining a distribution of user-specificasleep values based on the measured values of the physiologicalfeatures, and associating the distribution of user-specific asleepvalues to the user being asleep; and after the initialization stage:determining the user is asleep when a measured value of thephysiological feature extracted from a real-time physiological signal isin the distribution of user-specific asleep values, and altering anaudio experience for the user in response to determining the user isasleep based on the measured value of the physiological featureextracted from the real-time physiological signal being in thedistribution of user-specific asleep values.

According to aspects, the sensor signal comprises one of: anaccelerometer signal, photoplethysmogram (PPG) signal, a radar signal,or any other sensor signal capable of detecting the physiologicalfeature. According to aspects, the physiological signal is a respirationwaveform. According to aspects, the physiological feature comprises oneof: a respiration rate (RR), ratio of time to inhale to time to exhale,depth of breath, heart rate (HR), heart rate variability (HRV), bodymovement, or any other physiological feature that changes between wakeand sleep.

According to aspects, the initialization stage lasts for apre-determined amount of time.

According to aspect, the method further comprises one or more sensorsentering a low-power mode in response to determining the user is asleepbased on the measured value of the physiological feature extracted fromthe real-time physiological signal being in the distribution ofuser-specific asleep values.

According to aspects, the method further comprises determining the useris awake when a measured value of the physiological feature extractedfrom the real-time physiological signal is outside the distribution ofuser-specific asleep values, and altering the audio experience for theuser in response to determining the user is awake.

According to aspects, altering the audio experience for the user inresponse to determining the user is awake comprises initiating anexperience to help guide the user to sleep. According to aspects,altering the audio experience for the user in response to determiningthe user is awake comprises: in response to determining the user hasbeen awake for a threshold amount of time, instructing the user to standup. According to aspects, the method further comprises outputting atleast one of: time-to-sleep onset or how many times the user awokeduring a sleep period.

In an aspect, a method for creating a personalized audio experience isprovided. The method comprises, during an initialization stage:measuring a value of a physiological feature associated with a userbased on a sensor signal, determining the user is asleep usingpopulation-level data and the measured value of the physiologicalfeature, when the user is determined to be asleep, measuring values ofthe physiological feature extracted from a physiological signal obtainedusing the sensor signal, determining a distribution of user-specificasleep values based on the measured values of the physiologicalfeatures, associating the distribution of user-specific asleep values tothe user being asleep, determining the user is awake based on useraction, when the user is determined to be awake, measuring values of thephysiological feature extracted from the physiological signal obtainedusing the sensor signal, determining a distribution of user-specificawake values based on the measured values of the physiological features,and associating the distribution of user-specific awake values to theuser being awake, and after the initialization stage: determining theuser is asleep when a measured value of the physiological featureextracted from a real-time physiological signal is in the distributionof user-specific asleep values, determining the user is awake when ameasured value of the physiological feature extracted from the real-timephysiological signal is in the distribution of user-specific awakevalues, and altering an audio experience for the user in response todetermining the user is one of asleep based on the user-specificdistribution of asleep values or awake based on the user-specificdistribution of awake values.

According to aspects, the sensor signal comprises one of: anaccelerometer signal, photoplethysmogram (PPG) signal, a radar signal,or any other sensor signal capable of detecting the physiologicalfeature. According to aspects, the physiological signal is a respirationwaveform. According to aspects, the physiological feature comprises oneof a respiration rate (RR), ratio of time to inhale to time to exhale,depth of breath, heart rate (HR), heart rate variability (HRV), bodymovement or any other physiological feature that changes between wakeand sleep.

According to aspects, altering the audio experience for the user inresponse to determining the user is awake comprises initiating anexperience to help guide the user to sleep. According to aspects,altering the audio experience for the user in response to determiningthe user is awake comprises, in response to determining the user hasbeen awake for a threshold amount of time, instructing the user to standup.

All examples and features mentioned herein can be combined in anytechnically possible manner.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example population-level distribution ofrespiration rates during sleep and while awake.

FIG. 2 illustrates an example distribution of the respiration rate of anindividual while asleep and awake.

FIG. 3 illustrates example operations performed during an initializationstage, in accordance with aspects of the present disclosure.

FIG. 4 illustrates example operations performed during a steady-statestage, in accordance with aspects of the present disclosure.

FIG. 5 illustrates example operations performed during theinitialization stage and the steady-state stage, in accordance withaspects of the present disclosure.

DETAILED DESCRIPTION

Sleep plays an important role in overall physical health. As such, it ishelpful to understand an individual's sleep statistics such as time tosleep onset, wake after sleep onset, sleep efficiency, number ofwakeups, and duration of a sleep period. Further, when an individual hasfallen asleep, it is desirable for guided breathing or relaxationexercises to gradually adjust and fade out so that the individual is notexposed to unnecessary stimuli. In aspects, when an individual hasfallen asleep, it is desirable for masking sounds to be adjusted toprotect the individual's sleep. When the individual is determined to beawake, it is desirable to decrease or pause masking sounds, triggerguided breathing or relaxation exercises to help the user fall backasleep, alter audio output according to the individual's preference,and/or prompt the user to stand up if the user is determined to be awakefor a given amount of time.

Currently, population-level data is used to determine that a person isawake or asleep. In an example, based on human studies, physiologicalparameters are recorded when people are known to be asleep and awake.Population-level data is deployed for use with individual user devices.Sensor-collected data for an individual is compared with thepopulation-level data to determine that a user is awake or asleep. Whilesuch approaches are helpful to gain an understanding of how long a useris asleep (e.g., the individual slept for approximately 8 hours during asleep period), they are not robust enough to accurately determine inreal-time, or virtually near real-time, when the user has fallen asleepor when the user wakes up.

One reason why the current methods do not support precisely determiningsleep onset or wakeups is that individuals vary greatly in somephysiological measures while awake and similarly individuals varygreatly in some physiological measures while asleep. Therefore, it ischallenging to determine whether a specific person is asleep or awakeusing population-level data.

FIG. 1 illustrates an example population-level distribution ofrespiration rates (RRs) 100 during sleep and awake times. The x-axisrepresents the population-level RR and the y-axis represents theproportion of time a plotted value is observed. Line 102 illustrates thedistribution of RRs found in the population when awake. Line 104illustrates the distribution of RRs found in the population when asleep.The region 106 represents the overlap of population-level awake RRs andpopulation-level asleep RRs. Using population-level RRs as aphysiological feature (parameter) to determine if a specific individualis asleep or awake is problematic, in part, because the distribution ofRRs an individual exhibits, and consequently the population exhibits,when awake and asleep are wide and overlapping. Accordingly, awake andasleep values of the distribution of population-measured RRs may notclearly correlate with the distribution of an individual's awake andasleep RRs. As described herein, the distribution refers to the range ofpossible values as well as how often a measurement at a certain value isobserved.

FIG. 2 illustrates an example distribution of the RRs of an individual200 while asleep and awake. The x-axis represents the individual's RRand the y-axis represents the proportion of time a plotted value isobserved for the individual. Line 202 illustrates the distribution ofthe individual's RR when awake. Line 204 illustrates the distribution ofthe individual's RR when asleep. In FIG. 2 , there is little to nooverlap between the distribution of RRs when the individual is awake andthe distribution of RRs when the individual is asleep. Stated otherwise,on an individual-level, there is more separation between thedistribution of awake RRs and the distribution of asleep RRs as comparedto the population-level. Additionally, relative to FIG. 1 , thedistribution of the individual's RRs when awake or asleep is narrowerthan the population-level distribution of RRs when awake and asleep.Using an individual's personalized awake and asleep RRs more accuratelyindicates if the individual is awake or asleep. Further, based on thepersonalized awake and asleep distributions, sleep onset is moreaccurately determined.

FIGS. 1 and 2 refer to RR as an example of a measured physiologicalfeature used to estimate when an individual is awake or asleep.Non-limiting examples of physiological features that can be measured atthe population-level and individual-level to determine when a person isawake or asleep include a respiration signal, heart rate (HR), HRvariability (HRV), body movements, and elements of breath architecture.Similarly, any other physiological feature that changes between wake andsleep can be used in accordance with the methods described herein. Arespiration signal includes the different components of respirationincluding, for example, a RR and captures features of a heart signal.Breath architecture includes any combination of the ratio of time spentinhaling versus exhaling, how long a user breathes in, and the depth ofthe user's breath.

In one example, a device, such as an audio output device includes amemory and processor, communication unit, a transceiver, a sensor, and aspeaker or audio output transducer. The audio output device isconfigured to collect and/or use personalized physiological parametersto precisely determine when a user falls asleep or awakes. Any or all ofthe components may be combined into multi-function components. Inanother example, the memory and processor, communication unit,transceiver, sensor, and speaker or audio output transducer are includedin combination devices and/or the cloud in a sleeping environment. Thedevices communicate, in aspects, via a wired connection, the internet,or cloud-based communication, to perform the techniques describedherein.

The memory may include Read Only Memory (ROM), a Random Access Memory(RAM), and/or a flash ROM. The memory stores program code forcontrolling the memory and processor. The memory and processor controlthe operations of the audio output device, and optionally, other devicesin the sleep environment, as described herein.

The processor controls the general operation of the audio output deviceand/or other devices in the sleep environment. For example, theprocessor performs process and control for audio and/or datacommunication. The processor is configured to perform operations duringthe initialization stage using population-level data to determine when auser is awake or asleep, collect personalized physiological data, andcorrelate the collected personalized data with the user's awake orasleep state as described herein. After the initialization stage, theprocessor is further configured to direct operations during thesteady-state stage using real-time sensor-collected data to determinethe user has fallen asleep or has woken up as described herein.

In combination with the audio output transducer, the processor isconfigured to output audio which can take the form of a relaxationexercise, guided breathing exercise, or any other audio output eitheralone or in combination with, haptics or lights.

In at least one example, the processor is disposed on another device,such as a smartphone or audio output charging case and is incommunication with the audio output device.

The audio output device optionally includes a communication unit thatfacilitates a wireless connection with one or more other wirelessdevices. The communication unit may include one or more wirelessprotocol engines such as a Bluetooth engine. While Bluetooth is used asan example protocol, other communication protocols may also be used.Some examples include Bluetooth Low Energy (BLE), NFC, IEEE 802.11,WiFi, or other local area network (LAN) or personal area network (PAN)protocols. In aspects, the communication unit receives informationassociated with the user's physiological parameters, obtained via acontactless sensor. Examples of contactless sensors include a radiofrequency (RF) sensor, a radar sensor, or an under-bed accelerometer.

The transceiver transmits and receives information via one or moreantennae to exchange information with one or more other wirelessdevices. The transceiver may be used to communicate with other devicesin an audio system, such as a bedside unit, a smartphone, charging case,and/or a smartwatch. The transceiver is not necessarily a distinctcomponent.

The audio output device includes the audio output transducer, which maybe also known as a driver or speaker.

The audio output device optionally includes one or more sensors used todetermine, sense, measure, monitor, or calculate a physiological featureof a subject wearing the audio output device. In an example, the sensoris an accelerometer, a PPG sensor, or a radar sensor. The accelerometercollects an accelerometer signal from which various physiologicalfeatures are measured, estimated or extracted. The PPG sensor collects aPPG signal for which various physiological features are measured,estimated or extracted. The radar sensor collects a radar signal fromwhich various physiological features are measured, estimated orextracted. Any sensor that collects a signal from which a physiologicalfeature may be estimated to determine if a user is awake or asleep canbe used in accordance with the methods described herein.

On a high-level, during the initialization period, a distribution ofuser-specific asleep values for a physiological feature when a user isdetermined to be asleep is determined. Additionally, a distribution ofuser-specific awake values for the physiological feature when the useris determined to be awake is determined. A sensor is used to measurevalues of the physiological feature to determine the distribution.Referring to FIG. 1 , RR is an example of a physiological feature. In anexample, the user's RR is measured and compared to the population-leveldistributions illustrated in FIG. 1 . Based on the measured RR, the useris determined to be one of awake or asleep. Thereafter, one or moresensors measure the user's RR to determine a personalized distributionof RR for when the user is determined to be awake and determined to beasleep. During the initialization period, the method is learning thetypical values of the physiological features a specific individualdisplays when awake and asleep, using population-level data as astarting point.

After completion of the initialization stage, the process shifts to asteady-state stage. On a high-level, during the steady-state stage, theuser's real-time measured physiological features are compared with theuser-specific distribution of values (as determined in theinitialization stage) to determine if this specific user is awake orasleep. In response to determining the user is awake, the audio outputdevice may begin an experience to guide the user to sleep. For example,the audio output device may initiate a relaxation or guided breathingexercise. In aspects, the experience includes audio in combination withvisual output and/or haptic output. When the user is determined to beawake for a threshold amount of time (e.g., 20 minutes), the audiooutput device may coax the user to stand up through audio, visual,and/or haptic output, in an effort to promote healthy sleep hygiene. Inresponse to determining the user has fallen asleep, one or more sensorson the audio output device or a device in communication with the audiooutput device may promptly enter a low-power mode. For example, a userexperience may require the use of sensors on the audio output device.When the user is asleep, the experience may be paused. Accordingly, thedevice may save power when one or more components or sensors enter alow-power state. In aspects, when the user is determined to be asleep,the audio device may adjust an audio output in an effort to protectsleep and not expose the user to unnecessary stimulus.

FIG. 3 illustrates example operations 300 performed during aninitialization stage, in accordance with aspects of the presentdisclosure. The operations 300 can be performed by any combination ofthe audio output device, a contactless sensor, a wireless device, andthe cloud during an initialization period. The initialization period maylast for a pre-determined amount of time, such as configured number ofsleep periods or a configured number of days.

During an initialization period, a user's personalized physiologicalinformation is collected and compared to population-level data.Specifically, at 302, a value of a physiological feature associated witha user based on a sensor signal is measured. Example features include, aRR, ratio of time to inhale to time to exhale, depth of breath, HR, HRV,body movement, and/or or any other physiological feature that changesbetween wake and sleep. In aspects, one or more of an audio outputdevice or a contactless sensor is used to measure the value of thephysiological feature.

At 304, the user is determined to be asleep using population-level dataand the measured value of the physiological feature. In an example,sensor data is compared to population-level data as illustrated in FIG.1 and the user is determined to be asleep when the measured value of thephysiological feature corresponds to an asleep value in the asleepdistribution.

Given the user is determined to be asleep, at 306, a sensor obtains asensor signal that is used to measure, determine, estimate, or extractvalues of the user's physiological feature from a physiological signalobtained using the sensor signal. At 308, a distribution ofuser-specific asleep values are determined using the measured values ofthe physiological features when the user is determined to be asleep(based on the population-level data from step 304). At 310, thedistribution of personalized, user-specific asleep values are associatedwith the user being asleep. This distribution will be used in thesteady-state stage as described with reference to FIG. 4 and FIG. 5 toprovide the user with a timely, personalized audio experience.

According to aspects, the sensor signal comprises an accelerometersignal, a PPG signal, a radar signal, or any other sensor signal capableof detecting the physiological feature of interest. Regardless of thetype of sensor signal, physiological parameters can be estimated,measured, or extracted from the sensor signal. In an example, thephysiological signal is a respiration waveform.

FIG. 4 illustrates example operations performed during a steady-statestage 400, in accordance with aspects of the present disclosure. Theoperations 400 can be performed by any combination of the audio outputdevice, a contactless sensor, a wireless device, and the cloud. Duringthe steady-state stage, the audio output device (or a device incommunication with the audio output device) has acquired the user'spersonalized asleep and awake physiological distributions. The user'spersonalized data is used to precisely determine sleep onset and whenthe user wakes to provide the user with a timely, customized experiencesand accurate health statistics.

At 402, the audio output device, wireless device, or the clouddetermines the user is asleep when a measured value of the physiologicalfeature extracted from a real-time physiological signal is in thedistribution of user-specific asleep values (the user-specific valueswere calculated at 308 in FIG. 3 ). At 404, the audio experience for theuser is altered based on determining the user is asleep.

Optionally, at 406, the audio output device, wireless device, or thecloud determines the user is awake when a measured value of thephysiological feature extracted from the real-time physiological signalis outside the distribution of user-specific asleep values. Optionally,at 408, the audio output device alters the audio experience for the userin response to determining the user is awake. In aspects, when the useris determined to be awake steps are taken to help relax the user andguide the user back to sleep. In other aspects, the user is prompted tostand up. In aspects, based on the personalized determination of sleeponset and when the user wakes, more accurate sleep statistics areoutput. The sleep statistics can be used for potential healthinterventions and be used as additional information to guidingrecommended therapies and content.

FIG. 5 illustrates example operations 500 performed during theinitialization stage and steady-state stage, in accordance with aspectsof the present disclosure. The operations 500 can be performed by anycombination of the audio output device, a contactless sensor, a wirelessdevice, and the cloud.

Steps 502-518 occur during the initialization stage, when user-specificdata is collected in order to determine personalized values ofphysiological parameters. Steps 520-524 occur during the steady-statestage, when real-time user-specific sensor data is used to determine theuser is awake or asleep.

At 502, a sensor is used to measure a value of a physiological featureassociated with a user based on a sensor signal. At 504, the user isdetermined to be asleep using population-level data and the measuredvalue of the physiological feature. In aspects, the measured value ofthe physiological feature is compared to the population-level data todetermine the user is asleep.

At 506, when the user is determined to be asleep, values of thephysiological feature extracted from a physiological signal obtainedusing the sensor signal are measured. At 508, a distribution ofuser-specific asleep values based on the measured values of thephysiological features are determined. At 510, the distribution ofuser-specific asleep values are associated with the user being asleep.These steps are similar to those described in FIG. 3 .

At 512, the user is determined to be awake based on user action. At 514,when the user is determined to be awake, values of the physiologicalfeature extracted from the physiological signal obtained using thesensor signal are measured. In aspects, user action includes the userinteracting with the audio output device or wireless device (e.g.,pressing a button, toggling through menus) or sensor collected dataindicating the user is standing up.

At 516, a distribution of user-specific awake values based on themeasured values of the physiological features are determined. At 518,the distribution of user-specific awake values are associated with theuser being awake.

In an example, instead of using user action to determine the user isawake as shown in step 512, the user is determined to be asleep usingpopulation-level data and a measured value of a physiological feature.For example, a measured RR is compared to population-level data todetermine the measured RR corresponds to an awake RR based onpopulation-level data. Thereafter, the process proceeds to step 516 asdescribed above.

After a predetermined amount of time has passed, the method transitionsto the steady-state stage. In the steady-state stage, the audio outputdevice, wireless device, or cloud has enough personalized physiologicalinformation, specific to the user, to identify when sleep onset hasoccurred or when the user is awake.

At 520, the user is determined to be asleep when a measured value of thephysiological feature extracted from a real-time physiological signal isin the distribution of user-specific asleep values. At 522, the user isdetermined to be awake when a measured value of the physiologicalfeature extracted from the real-time physiological signal is in thedistribution of user-specific awake values. At 524, an audio experienceis altered in response to determining the user is asleep based on theuser-specific distribution of asleep values or awake based on theuser-specific distribution of awake values.

Aspects of the present disclosure leverage population-level data tounderstand when a user is asleep or awake. Thereafter, user-specificphysiological features are measured to understand precisely when theuser is awake or asleep. In some cases, the techniques described hereinaccurately determine, by the minute, when the user has fallen asleep orwoken up. After personalized data is collected over a period of time,the user's distribution of awake and asleep physiological parameters areknown. Thereafter, the method determines the user is awake or asleepbased on the user's determined distribution instead of thepopulation-level data to provide a tailored user experience and moreaccurate sleep statistics.

In the preceding, reference is made to aspects presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described aspects. Aspects of the present disclosure maytake the form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “component,” “circuit,” “module” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples of a computer readable storage medium include: anelectrical connection having one or more wires, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the current context, a computer readable storage medium may be anytangible medium that can contain, or store a program.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality and operation of possible implementations ofsystems, methods and computer program products according to variousaspects. In this regard, each block in the flowchart or block diagramsmay represent a module, segment or portion of code, which comprises oneor more executable instructions for implementing the specified logicalfunction(s). In some implementations, the functions noted in the blockmay occur out of the order noted in the figures. For example, two blocksshown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. Each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations can beimplemented by special-purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The invention claimed is:
 1. A method for creating a personalized audioexperience to promote sleep health, comprising: determining adistribution of user-specific asleep values for a physiological featurewhen a user is determined to be asleep; determining a distribution ofuser-specific awake values for the physiological feature when the useris determined to be awake; determining the user is asleep when ameasured value of the physiological feature extracted from a real-timephysiological signal is in the distribution of user-specific asleepvalues; determining the user is awake when a measured value of thephysiological feature extracted from the real-time physiological signalis in the distribution of user-specific awake values; determining theuser has been awake for a threshold amount of time; and altering anaudio experience based on determining the user is asleep or awake viathe measured value of the physiological feature being in thedistribution of user-specific awake values or the user-specific asleepvalues, wherein altering the audio experience comprises instructing theuser to stand up, via at least one of audio, video, or haptic output,when the user is determined to be awake for the threshold amount oftime.
 2. The method of claim 1, further comprising: entering a low-powermode by one or more sensors in response to determining the user isasleep.
 3. A method for creating a personalized audio experience topromote sleep health, comprising: during an initialization stage:measuring a value of a physiological feature associated with a userbased on a sensor signal; determining the user is asleep usingpopulation-level data and the measured value of the physiologicalfeature; when the user is determined to be asleep, measuring values ofthe physiological feature extracted from a physiological signal obtainedusing the sensor signal; determining a distribution of user-specificasleep values based on the measured values of the physiological featuresto create a more personalized distribution of asleep values as comparedto the population-level data; and associating the distribution ofuser-specific asleep values to the user being asleep; and after theinitialization stage: determining the user is awake when a measuredvalue of the physiological feature extracted from the real-timephysiological signal is outside the distribution of user-specific asleepvalues; and altering an audio experience for the user in response todetermining the user is awake based on the measured value of thephysiological feature extracted from the real-time physiological signalbeing outside the distribution of user-specific asleep values, whereinaltering the audio experience comprises instructing the user to standup, via at least one of audio, video, or haptic output, when the user isdetermined to be awake.
 4. The method of claim 3, wherein the sensorsignal comprises one of: an accelerometer signal, photoplethysmogram(PPG) signal, a radar signal, or any other sensor signal capable ofdetecting the physiological feature.
 5. The method of claim 3, whereinthe physiological signal is a respiration waveform.
 6. The method ofclaim 3, wherein the physiological feature comprises one of: arespiration rate (RR), ratio of time to inhale to time to exhale, depthof breath, heart rate (HR), heart rate variability (HRV), body movement,or any other physiological feature that changes between wake and sleep.7. The method of claim 3, wherein the initialization stage lasts for apre-determined amount of time.
 8. The method of claim 3, furthercomprising: outputting at least one of: time-to-sleep onset or how manytimes the user awoke during a sleep period.
 9. A method for creating apersonalized audio experience to promote sleep health, comprising:during an initialization stage: determining a user is awake based onuser action; when the user is determined to be awake, measuring valuesof a physiological feature extracted from a physiological signalobtained using a sensor signal; determining a distribution ofuser-specific awake values based on the measured values of thephysiological feature to create a more personalized distribution ofawake values as compared to the population-level data; and associatingthe distribution of user-specific awake values to the user being awake;and after the initialization stage: determining the user is awake when ameasured value of the physiological feature extracted from the real-timephysiological signal is in the distribution of user-specific awakevalues; determining the user has been awake for a threshold amount oftime; and altering an audio experience for the user in response todetermining the user is one of asleep based on the user-specificdistribution of asleep values or awake based on the user-specificdistribution of awake values, wherein altering the audio experiencecomprises instructing the user to stand up via at least one of audio,video, or haptic output.
 10. The method of claim 9, wherein the sensorsignal comprises one of: an accelerometer signal, photoplethysmogram(PPG) signal, a radar signal, or any other sensor signal capable ofdetecting the physiological feature.
 11. The method of claim 9, whereinthe physiological signal is a respiration waveform.
 12. The method ofclaim 9, wherein the physiological feature comprises one of: arespiration rate (RR), ratio of time to inhale to time to exhale, depthof breath, heart rate (HR), heart rate variability (HRV), body movementor any other physiological feature that changes between wake and sleep.